According to the World Meteorological Organization’s State of Climate Services 2020Opens a new window report, more than 11,000 disasters in the last half-century have been attributed to weather, climate, and water-related hazards, involving 2 million deaths and $ 3.6 trillion in economic losses. â€œAI has the potential to help all countries to achieve major advances in disaster management that will leave no one behind,â€ according to JÃ¼rg Luterbacher, Chief Scientist and Director of Science and Innovation at WMO.
As an example, the recent winter storms throughout Texas caused massive power failures and a water crisis. Initially, business disaster recovery plans handled these issues using backup generators; however, businesses began to fail as the crisis extended into days and weeks. Their plans had not taken into account this extreme weather event.
One lesson learned from this crisis is that IT decision makers (ITDMs) must take a more proactive approach to deal with severe weather. There are several technology-based remedies that management can integrate into disaster recovery planning. One of these uses machine learning (ML) and artificial intelligence (AI) solutions that combine weather prediction algorithms with risk analysis and business continuity management to forecast extreme weather events and provide warnings and support disaster relief efforts.
The Art of Business Resilience Planning (BRP)
Planning for contingencies that can affect your business spans the entire enterprise. Affected areas include all of your IT infrastructure, your product supply chain, workforce planning (both local and remote) and financial analysis. AI is a natural fit for BRP, as all of these areas involve high volumes of transactional data that can be gathered, modeled, and analyzed for patterns. For example, in financial planning and analysis, today’s support platforms must execute standard processes and drive business agility. Issues that affect the business will always have costs, and mitigating risks requires monetary investments.
BRP requires that you create a culture of resilience. Adapt your reactive processes to be more proactive, and institute continuous predictive planning. Risk mitigation decisions need to be quickly made and fact-based. Implementing AI-based systems can ease this migration.
Sue Ellen Haupt, senior scientist and deputy director of the Research Applications Laboratory at the U.S. National Center for Atmospheric Research (NCAROpens a new window ) in Boulder, Colorado, notes, â€œUsing AI for forecasting isn’t new, but the push to use it more and to use it differently is new. We’re beginning to use AI to determine what storms will have extreme events, like hail or tornadoes. We might be able to get more than a few minutes’ warning. We hope to maybe even get hours. AI is going to be the key to better forecasting.â€
NCAR, the U.S. National Weather ServiceOpens a new window and the National Oceanic and Atmospheric Administration (NOAAOpens a new window ) already use significant IT resources to forecast weather and climate conditions. The Dynamic Integrated foreCast (DICastOpens a new window ) system was developed in the early 2000s and incorporates AI.
Other organizations around the world are working on various planning processes to incorporate AI into weather prediction.
- The World Meteorological Organization (WMO) is participating in a new interdisciplinary focus groupOpens a new window to contend with the increasing prevalence and severity of natural hazards with the help of AI;
- The International Telecommunications Union (ITUOpens a new window ) has a focus group on AI for Natural Disaster Management in partnership with WMO and the United Nations Environment Programme;
- IBM is hosting a Plan for Business ResilienceOpens a new window conference that includes panel discussions and breakout sessions concentrating on using AI and proactive methods to avoid business disruption.
AI and Weather Forecasting
Predictive weather AI algorithms such as those used by Penn State University (PSUOpens a new window ) are more than just a process for weather forecasting. The underlying machine learning models are based not only on historical weather data and current conditions but also incorporate information about your business. This can include the effects of specific weather events and their severity on your supply chain, workforce, and business continuity processes.
Consider the prelude to a major event. AI models can incorporate data from social media, news reports and public weather reporting. This data is then cross-indexed against historical trends to highlight the probability of severe weather and model the effects on infrastructure and people. If a model predicts possible events, the event specifics, including severity, duration, and geographic location, can notify affected areas. A proactive AI system can also connect to other systems that receive warning and execute protective actions such as scheduling salt trucks, alerting infrastructure support technicians, evacuating populations, powering up alternate IT sites and even signaling some employees to either leave an area or take shelter.
AI can also assist in post-disaster relief. AI models can gather data on geographical areas affected by weather events and provide updated forecasts on the weather. AI can even assist call centers in helping emergency workers in responding to situations.
Incorporating Weather AI into Business Resilience Planning
In 2021, the Disaster Recovery JournalOpens a new window noted that organizations could upgrade their operational resilience plans in 2021 with risk intelligence. A recent articleOpens a new window noted, â€œBusiness leaders now know they need to run their organizations in a state of constant risk identification and mitigation: planning for the unpredictable now, more than ever, must be business as usual.â€
Risk intelligence is a combination of incident planning management and critical communications strategies that are forward-looking and resilient. Integrating a weather prediction AI system into your business resilience planning is a part of this strategy.
To begin, review your current business continuity plan or disaster recovery plan. Most plans contain sections with pre-scripted processes for reacting to various events that negatively affect your organization. These include IT-related equipment failures, local power outages and damage to specific infrastructures such as buildings or communication lines. Review parts of your plan where there is historical data available about resource outages, recovery time objectives (RTO), time-to-recovery and costs. This data can serve as a starting point for machine learning models.
Another parallel step is to discover and review available weather prediction software. For example, IBM provides outage prediction as a result of its acquisition of The Weather Company. Such software or services is best implemented in a distributed cloud environment rather than in a single location that might be affected by an event.
As noted previously, you must re-tool your support organization to be less reactive and more proactive. Simply responding to events is not enough; you must do risk assessments now so that you can plan ahead for any investments required for risk mitigation. This requires your organization to be agile, adaptive and resilient. A fact-based AI system can be a useful tool for disaster planning and business continuance in case of weather-related disasters.
Do you think predictive weather insights can strengthen your disaster recovery strategy?Â Let us know your thoughts in the comment section below or on LinkedInOpens a new window , TwitterOpens a new window , or FacebookOpens a new window . We would love to hear from you!Â